Triple
T4425387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JAX |
E95194
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | jax.numpy |
E17659
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: jax.numpy | Statement: [JAX, hasComponent, jax.numpy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: jax.numpy Context triple: [JAX, hasComponent, jax.numpy]
-
A.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
B.
NumPy
chosen
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
C.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
D.
XLA
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes and accelerates machine learning computations on hardware such as TPUs and GPUs.
-
E.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554e40ec8190982acc0948da2f42 |
completed | March 13, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f633a69c8190b062c2a78b0f8319 |
completed | March 14, 2026, 11:58 p.m. |
Created at: March 12, 2026, 11:30 p.m.